Data-as-a-Service: Data or data management as a core underlying asset; services are created on top of data that is collected, trained, and labeled 

Business Models in Use

Moving Beyond Insights and Analytics

Much of data strategy and delivery has been constrained and siloed within the Business Intelligence function inside of companies. Data-as-a-service models move beyond merely enhancing data processes and analytics to inform internal decisions and moves to create value for end customers outside the organization.s.


Data Value Chain

Data-as-a-Service Evaluator

Valuable to Customer



Key Performance Indicators

Valuable to Business Offering PaaS



Key Performance Indicators

When it Works Well

Fills Critical Data and Data Quality Gaps

Business data used to be generated primarily from inside of a company, and from interaction directly with customers. Data sources were static, structured, and small enough to be stored or digitally warehoused inside of a company (often in physical servers).

Now most data lives outside of a company’s walls, the data is big (big data!), which means there is too much, too fast, with too much variety to be stored in house. Consumers, sensors, and machines – most of them outside the walls of the enterprise – are now generating most of the data in the world. The data has different value and different uses, and the data should do the work of data preparation: fill data gaps, filter, clean, aggregate, and simplify data access for the customer.

Companies can generate their own dataset and sell access (Factual), build one of the most effective access points and sell access (Bloomberg), or help customer add external data to their datasets (Zephyr Health or Socrata).

Feed a Data Chain that Drives Revenue Growth or Cost Reduction

Ultimately the data should feed into a core business function that drives revenues up and/or costs down. The more the data service can feed a system that clearly delivers better top line revenue, the more valuable the offer and the business.

Advances in data storing, aggregation, processing and machine learning create new opportunities for data not just to feed into a report of past activity, but to predict future outcomes. Data is now a powerful source of strategic advantage and may even be repackaged by a customer to provide an additional data-as-a-service.

Integrates Into a Workflow 

Data-as-a-service models typically do not disrupt a core process, but instead augment a workflow. Customer experience journeys and deep understanding of industry needs help to immediately provide value to the business. Few customers ask for an additional dashboard, so data services companies often follow a default design strategy to integrate into the day of the end-user, and a well-defined customer problem. 

Builds Moats Around the Data

As software and hardware are commoditized, deriving value from meaningful datasets remains a core source of competitive advantage. While many leading software-driven companies open source their management, infrastructure and monitoring tools; they tend to keep their data as a primary asset. Because it is difficult to copy a dataset without a large user base, companies focus on building a moat around the data.

Challenge to the DaaS Model

Diminishing Returns for Once Valuable Proprietary Datasets

Data collection and supply is not a new business. Experian and Axciom are public companies that have been amassing large data stores to sell to companies, and these firms are infamous for the decisions they inform: ad targeting, mortgage rates, credit card terms, insurance premiums, and employment offers.

But the rise in the huge volume of new data sources and varieties diminishes the value of data collected in these proprietary data gatherers. Additionally, new entrants experiment with new business models – Facebook is a generator of unique descriptive data on every engaged user, and provides highly sophisticated ad buying services to customers for free, as an insight, targeting, and advertising efficacy tool. As business model innovation continues at an accelerated rate, expect traditional business metrics and the dominant logic of the industry to shift.

Customers May Want to Own the Data

You may have designs on a future data asset to power a massive data service, but your customers may not want you to collect that data. Developing agreements based on anonymized data, or developing revenue share models may help you to mitigate this challenge.

New Privacy and Data Ownership Regulation 

Governments at the national and state level have put specific data ownership and privacy standards in place. Following these regulations to their highest standards has become costly for startups, and often benefits incumbents. The General Data Protection Regulation (GDPR) implemented in 2018 has higher standards for any company collecting user data. New opportunities emerge for more open published standards on data use, and for new entrants that address user ownership of data as a fundamental core assumption within the business model. 

Margins Uglier in the Beginning

Because of the way that data costs are accounted for early on investments to acquire data result in high cost of goods sold (COGS) and therefore high margins. However these margins do indeed increase over time. But investors used to seeing SaaS margins may overlook an opportunity to invest in a data services company at the start. 

Data Exhaust and Surveillance Capitalism Fears

Because tech giants have been less than transparent for how they collect, store, barter, and sell data there is emerging concern that these companies are undermining personal autonomy and eroding democracy. We would argue that the business model matters. Companies that collect data exhaust or data sources that use advertising as their primary business model operate very differently than companies with subscription-based or more diverse business models. 


Trends in DaaS

Self-Service Insights

Data-driven companies are going through a renaissance of experimentation, as new startups and existing firms experiment with new data business models. Many new firms are moving upstream to specialize in data visualization, data-driven insights and decision tools, providing directly to business analysts and decision makers, and breaking down the central hierarchy of the CTO, CIO, and information technology staff. Companies are also designing end customer facing data-driven insights with the advent of sensors in everything: golf clubs that inform you how to improve your game, or sensor tags helping you find your keys.

Automated Insights

As insights move upstream, the connection of everything to everybody means that previously invisible processes can now be tracked and optimized. Everything that can be automated will be automated, replacing analog and human processes. As Amazon’s CFO Brian Olsavsky said in the Second Quarter 2015 financial report, “We’re using software and algorithms to make decisions rather than people, which we think is more efficient and scales better.” Uber is currently looking for engineers who are excited by such as prospect: “Do you enjoy having your software make critical decisions rather than just “process” or “display”?

Our prediction: Everything that can be digitized will be digitized, and every aspect of a company’s business model will be transformed by the adoption of data-driven organization design.

Data Commons

There are emerging industry, foundation, not-for-profit and non governmental organization solutions to collect and managing data that is considered a common good. 

Key DaaS Mechanisms to Test

KPIs depend on your unique business attributes and business model combinations. However there are heuristics when investors evaluate a data-as-a-service model:

Before You Consider DaaS

  • Where does the solution fall on the data-knowledge-wisdom hierarchy? 
  • Test for jobs to be done, level, pain on the pain scale. How much of a priority is this solution? 
  • Is your data ready to be used? Do we require additional data from the customer, third parties, or open source data projects?
  • What is the culture of data informed decisions within our customer base? 
  • Are our customers data-literate? Can they review Python libraries or dashboards? Do they need to review everything in Powerpoint with an analyst at the ready? 
  • What decisions will be affected by our data solution? 

Testing the Model

  • What is the total cost of ownership of comparable solutions? 
  • Arrange features, services, and benefits into key elements of your offer and have the potential customer arrange the elements of the larger solution in order of priority. Then take away the lesser priority elements until you determine what would make an MVP (minimum viable product). 
  • Determine the minimal offering that would be compelling enough to have the customer pay for the offering.
  • Can you design an MVP on paper, PowerPoint, or PDF?
  • Does your initial data offering have high usage and engagement with a minimal feature set? 
  • Is there a user proposition that does not require sign-off from IT or a long buying cycle? 

More on DaaS

Value of Data: Using Data as a Competitive Advantage, by Lee Polovets at Susa Ventures, 2015.

Data Driven Business Models, Cambridge Service Alliance, 2014. (PDF)

Data Markets and the Monetisation of Data Goods, Intereconomics, 2019

Data-As-A-Service Bible: Everything You Wanted To Know About Running DaaS Companies, by Auren Hoffman, Safegraph, 2019. 

Never Sell Data, by Steve Faktor,, 2014.

The Attributes that Define the Increasingly Critical Data-as-a-Service Industry, by Alex Rosen, TechCrunch, 2018. 

A Review of the Data Broker Industry: Collection, Use, and Sale of Consumer Data for Marketing Purposes, Staff Report for Chairman Rockefeller, Committee on Commerce, Science and Transportation, US Senate, 2013 (PDF)

The Morning Download Amazon CFO Says Algorithm-Based Decision Making Helped Company Achieve Profit, by Steve Rosenbush, The Wall Street Journal, 2015.

High tech is watching you, by Shoshana Zuboff, NYT, 2019.

Personal Data as an Asset Class, World Economic Forum, 2011. (PDF)

Data is the New Crude, by Michael Palmer, ANA Maestros, 2006.

Data-as-a-Service, by Rocketsource, 2017

Big Data is Not the New Oil, by Jer Thorpe, Harvard Business Review, 2012. (limited access)

Business Models for the Data Economy, by Q Ethan McCallum and Ken Gleason, O’Reilly Media, 2013 (download)

Everything We Wish We’d Known about Building Data Products, First Round Review, 2015.

Data As A Service: The Big Opportunity For Business, by Daniel Newman, Forbes, 2017